Although this is a common request, several personal definitions of "downscaled climate projections" exist. For example, the term downscaled data is frequently used by decision makers to mean locally-relevant climate information (past or future), whereas others mean future climate model simulations that have been downscaled. In cases where downscaled climate model simulations are appropriate there are important decisions related to which data set(s) should be used based on the types of questions being asked. Most decision makers do not end up needing or wanting downscaled data once they learn how it is intended to be used, because many applications do not require quantified climate variables. Many decision makers find that a best-available synthesis of the data for their particular application is sufficient.

GLISA has developed the following tool to serve those seeking guidance on climate information to use in decision making. This tool is intended to help climate information users:

better understand the types of climate information that are available

determine if they really need downscaled climate projections or if another information product is sufficient

have the opportunity to contact GLISA for additional guidance if needed

First, we (GLISA) present some definitions of the types of available climate information so information users and producers can communicate about needs more effectively.

Climate Information -- This is the most general term and includes information coming from climate observations, climate model projections (including downscaled climate projections), climate impacts data/narratives, and climate scenarios.

Climate Observations -- Climate observations can be historical time series for individual weather stations or, alternatively, the station observations can be used as an input to develop a gridded historical dataset or a reanalysis dataset that is based on a model's simulation.

Climate Projections -- Coarse spatial scale computer simulations from global climate models (GCMs) or fine spatial scale information produced either by regional climate models (RCMs) or other downscaling techniques. The model simulations include both the past to assist in evaluation and interpretation of the model quality as well as the projected future. This definition works well with our stakeholders. Experts in the field, however, make distinctions between climate predictions and projections. Predictions describe an estimate of the actual evolution of the climate, and projections also vary socio-economic and technological development. (PDF glossary of climate terms)

Climate Scenarios -- Climate scenarios can take several forms, but generally speaking they are a "what if" narrative about future climate typically derived from climate projections. They are not a predictive tool, rather, they can be used to think about possible futures.

Climate information seekers, please proceed through the steps in this tool to guide you to foundational resources for your problem. The first two steps are used to better define the user's climate information needs and step 3 is intended to present the user with relevant climate information resources based on their responses in step 1 and step 2.

Step 1: Determine the time frame for the climate application

Are you, the climate information user, interested in what has happened in your region, or are you interested in what will happen? Likely both of these questions will be important, but the source of information and their caveats are different for historical versus future climate information. If you are interested in what will happen with climate in your region, how far into the future are you planning? Planning horizons become important because climate information for 100 years into the future should be interpreted differently than for the next 10 years.

Step 2: Determine what type of information is required for the application.

GLISA divides climate information into two main categories: 1) data sets consisting of climate observations or projections and 2) analysis and synthesis products (see example products - climate data analysis and climate information synthesis). Analysis products are typically more limited in scope and are built on existing information that is reformatted to better communicate the climate information, such as presenting climate data in a narrative format. Synthesis products are an aggregation of several sources of existing information to create new information that is not available elsewhere. For example, a synthesis product could be bringing together climate observations and future climate projections, evaluating their information, and presenting a narrative of how climate has and will possibly change.

If a climate information user believes their application requires climate observations or projections they should ask themselves the following questions. A "yes" answer to at least one question in the following set may indicate that quantitative data are necessary for the application.

Do you have a model that requires climate data as an input? (i.e., hydrological model)

Does your decision making rely on quantitative inputs? (i.e., you need a specific number to make a calculation)

Are you planning to develop climate change scenarios?

Pathway 1: User wants observations or climate model projections

Users requesting observations or climate model projections should have the technical capacity to work with potentially large data sets. Typically a user will require this type of information because they are using climate data as an input to another model or decision that relies on a quantitative measure. Any application using quantitative climate information should elicit expert guidance on the interpretation of that information and its uncertainties.

Most climate applications will not require a strict quantitative value for decision making. Instead, qualitatively-based resources are available to guide climate information seekers. Climate analysis and synthesis products may be generic enough to serve wide audiences, but expert guidance should be sought to properly interpret each product in light of the specific application. In some cases products must be developed if there are none suitable for the application.

GLISA has matched important resources/products/data with the various types of information needs. These foundational resources are intended to get a user started in addressing their problem, but they may not be a complete source of knowledge for all climate applications. The Climate Information Guide (table below) can be used to access foundational climate resources GLISA recommends based on the responses in Step 1 and Step 2. Users, please guide yourself through the table after completing Step 1 and Step 2 to be directed toward climate information that best fits your application. If a user finds that information is incomplete for their specific problem they may contact GLISA and request additional assistance. After the Problem Description Form has been submitted GLISA will review the information and determine if the request can be satisfied with available data and resources. If not, GLISA will determine if there is an acceptable alternative.

Step 1: Primary Information Request

I want to know what has happened in my area

I want to know what will happen in my area

Step 2: Determine the type of information needed

I want historical climate observations.

This includes station data as well as gridded observational data sets.

I want a prepared historical climate summary.

This is a narrative about historical climate that may include maps and figures, but it is not a quantitative data record.

I want future climate projections.

Climate projections are quantitative output from climate model simulations or the products (i.e., downscaled version) of those simulations.

I want a summary of future climate.

Narratives summarizing future climate information are available for a variety of topics and sectors.

User Requirements

Users requesting raw climate data should have the capability to work with large data sets. Typically a user will require data because they are using it as an input to another model or decision that relies on a quantitative measure.

Existing climate analysis and synthesis products have been prepared for a wide range of audiences and applications, so we recommend users of that information have a means for interpreting the information in light of the problem that they are solving.

Users requesting raw climate data should have the capability to work with large data sets. Typically a user will require data because they are using it as an input to another model or decision that relies on a quantitative measure.

Existing climate analysis and synthesis products have been prepared for a wide range of audiences and applications, so we recommend users of that information have a means for interpreting the information in light of the problem that they are solving.